US20220309433A1 - Management support apparatus, management support method, and computer readable medium - Google Patents

Management support apparatus, management support method, and computer readable medium Download PDF

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Publication number
US20220309433A1
US20220309433A1 US17/840,315 US202217840315A US2022309433A1 US 20220309433 A1 US20220309433 A1 US 20220309433A1 US 202217840315 A US202217840315 A US 202217840315A US 2022309433 A1 US2022309433 A1 US 2022309433A1
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Prior art keywords
replacement
probability
deterioration
item
management support
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Hiroyuki Yamada
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Mitsubishi Electric Corp
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Mitsubishi Electric Corp
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Definitions

  • the present disclosure relates to a management support apparatus, a management support method, and a management support program.
  • the inventory quantity of items such as a device used in a system and parts which constitute the device has been managed relying on experience of a person. Then, in such a method of managing the inventory quantity based on the experience of the person, it is difficult to secure an experienced person who can manage the inventory quantity properly, and labor required for estimating the inventory quantity is a lot.
  • Patent Literature 1 JP2018-142256A
  • Patent Literature 1 predicts the number of malfunctions of items which are in use, using a cumulative malfunction rate of the items which are in use. Then, the necessary minimum inventory quantity of replacing items which replace the items which are in use is estimated based on the predicted number of malfunctions.
  • the cumulative malfunction rate is an expected value of a probability distribution of the number of malfunctions, probabilistic variability in the number of malfunctions is ignored.
  • Patent Literature 1 there is a possibility that an error has occurred in prediction of the number of malfunctions of the items which are in use, due to the probabilistic variability and the estimation accuracy of the inventory quantity is deteriorated.
  • Patent Literature 1 has a problem that the necessary minimum inventory quantity of replacing items cannot be properly estimated, which may result in an inventory shortage or excess inventory.
  • One of the main objects of the present disclosure is to solve the above-described problem, and the main object is to properly estimate the necessary minimum inventory quantity of replacing items and consequently, prevent an inventory shortage and excess inventory.
  • a management support apparatus includes:
  • a probability-distribution derivation unit to derive based on a measurement result obtained by measuring a deterioration degree resulted from usage of an item in a usage environment which uses the item which deteriorates with the usage, a probability distribution of a replacement quantity which is a quantity of the item required to be replaced at a replacement timing of the item;
  • a replacement-quantity calculation unit to calculate a replacement quantity with which a lower probability of the probability distribution of the replacement quantity is equal to or larger than a criterion value.
  • FIG. 1 is a configuration diagram of a management system according to a first embodiment.
  • FIG. 2 is a diagram illustrating a hardware configuration example of a management support apparatus according to the first embodiment.
  • FIG. 3 is a diagram illustrating a functional configuration example of the management support apparatus according to the first embodiment.
  • FIG. 4 is a flowchart illustrating an operation example of the management support apparatus according to the first embodiment.
  • FIG. 5 is a flowchart illustrating an example of calculation processes of replacement deterioration threshold values and deterioration-threshold-value probabilities by the management support apparatus according to the first embodiment.
  • FIG. 6 is a flowchart illustrating an example of a derivation process of a probability distribution of a replacement quantity by the management support apparatus according to the first embodiment.
  • FIG. 7 is a flowchart illustrating an example of a calculation process of the replacement quantity which satisfies a required reliability degree according to the first embodiment.
  • FIG. 8 is a diagram illustrating a functional configuration example of a management support apparatus according to a second embodiment.
  • FIG. 9 is a flowchart illustrating an operation example of the management support apparatus according to the second embodiment.
  • FIG. 10 is a flowchart illustrating an operation example of a management support apparatus according to a third embodiment.
  • FIG. 11 is a diagram illustrating a functional configuration example of a management support apparatus according to a fourth embodiment.
  • FIG. 12 is a flowchart illustrating an operation example of the management support apparatus according to the fourth embodiment.
  • FIG. 13 is a configuration diagram of a central management system according to a fifth embodiment.
  • FIG. 14 is a diagram illustrating a functional configuration example of a management support apparatus according to the fifth embodiment.
  • FIG. 15 is a flowchart illustrating an operation example of the management support apparatus according to the fifth embodiment.
  • FIG. 16 is a flowchart illustrating an example of a derivation process of a probability distribution of a replacement quantity by the management support apparatus according to the fifth embodiment.
  • FIG. 17 is a diagram illustrating a configuration in which functions of the management support apparatus are realized by hardware, according to the first embodiment.
  • FIG. 1 illustrates a configuration diagram of a management system 1 according to a first embodiment.
  • the management system 1 includes a management support apparatus 10 , devices 20 , sensors 30 , and a network 40 .
  • the management support apparatus 10 acquires pieces of measurement information regarding deterioration degrees of parts, and calculates the necessary minimum inventory quantity of replacing parts which replace the parts which are in use.
  • an operation procedure of the management support apparatus 10 is equivalent to a management support method. Further, a program which realizes operation of the management support apparatus 10 is equivalent to a management support program.
  • the devices 20 use the parts which deteriorate with usage.
  • the devices 20 are, as specific examples, devices used in social infrastructures such as railroad cars, a power plant, and an elevator. Also, the parts are, as a specific example, brake shoes.
  • the management support apparatus 10 calculates the necessary minimum inventory quantity of replacing parts corresponding to the parts of the same type and the same form which are used commonly in the plurality of devices 20 .
  • the sensors 30 are sensors or measurement tools which measure the deterioration degrees of the parts, attached to the devices 20 . Further, the sensors 30 transmit pieces of measurement information regarding the acquired deterioration degrees of the parts of the devices 20 to the management support apparatus 10 via the network 40 .
  • the sensors 30 are, as specific examples, abrasion sensors, oil deterioration sensors, or the like.
  • the network 40 is a wired or wireless communication path for transmitting and receiving data.
  • the network 40 is, as a specific example, a communication path conforming to a communication standard such as Ethernet (registered trademark) or Wi-Fi (registered trademark), or a communication path dedicated to the devices. Further, the network 40 may be a communication network such as an intranet or the Internet.
  • the quantity of devices 20 and the quantity of sensors 30 are assumed to be the same, but not limited to being the same, the device 20 may use a plurality of parts, and the sensor may be attached to each of the plurality of parts.
  • FIG. 2 illustrates a hardware configuration example of the management support apparatus 10 according to the first embodiment.
  • the management support apparatus 10 is a computer.
  • the management support apparatus 10 includes a processor 11 , a memory 12 , an auxiliary storage device 13 , an input/output interface 14 , and a communication interface 15 as pieces of hardware, which are connected to each other via a signal line.
  • the processor 11 is an IC (Integrated Circuit) which performs processing.
  • the processor 11 is, as a specific example, a CPU (Central Processing Unit), a DSP (Digital Signal Processor), or the like.
  • the memory 12 is a storage device which temporarily stores data.
  • the memory 12 is, as a specific example, a RAM (Random Access Memory).
  • the auxiliary storage device 13 is a storage device which stores data in a non-volatile manner.
  • the auxiliary storage device 13 is, as a specific example, a hard disk.
  • the auxiliary storage device 13 may also be a portable recording medium such as an SSD (registered trademark, Solid State Drive), an SD (registered trademark, Secure Digital) memory card, CF (registered trademark, CompactFlash), NAND flash, a flexible disk, an optical disc, a compact disc, a Blu-ray (registered trademark) disc, or a DVD (registered trademark, Digital Versatile Disk).
  • SSD Solid State Drive
  • SD registered trademark, Secure Digital
  • CF registered trademark, CompactFlash
  • NAND flash NAND flash
  • a flexible disk an optical disc
  • a compact disc compact disc
  • Blu-ray registered trademark
  • DVD registered trademark, Digital Versatile Disk
  • the auxiliary storage device 13 stores programs which realize functions of an information acquisition unit 100 and an information processing unit 110 which will be described later. Further, the auxiliary storage device 13 also stores a program which realizes a function of executing deterioration prediction which will be described later.
  • the programs stored in the auxiliary storage device 13 which realize the function of the information acquisition unit 100 , the function of the information processing unit 110 , and the function of executing the deterioration prediction are loaded by the memory 12 . Further, the programs are read and executed by the processor 11 .
  • the auxiliary storage device 13 also stores an OS (Operating System). Then, at least a part of the OS is executed by the processor 11 .
  • OS Operating System
  • the processor 11 While executing at least the part of the OS, the processor 11 executes the programs which realize the functions of the information acquisition unit 100 and the information processing unit 110 .
  • At least one of information, data, a signal value, and a variable value that indicate results of processes of the information acquisition unit 100 and the information processing unit 110 is stored in at least one of the processor 11 , the memory 12 , and a register and a cash memory in the auxiliary storage device 13 .
  • the programs which realize the functions of the information acquisition unit 100 and the information processing unit 110 may be stored in the portable recording medium such as the hard disk, the SSD (registered trademark), the SD (registered trademark) memory card, the CF (registered trademark), the NAND flash, the flexible disk, the optical disc, the compact disc, the Blu-ray (registered trademark) disc, or the DVD (registered trademark).
  • the portable recording medium such as the hard disk, the SSD (registered trademark), the SD (registered trademark) memory card, the CF (registered trademark), the NAND flash, the flexible disk, the optical disc, the compact disc, the Blu-ray (registered trademark) disc, or the DVD (registered trademark).
  • the portable recording medium storing the programs which realize the functions of the information acquisition unit 100 and the information processing unit 110 may be distributed.
  • the input/output interface 14 is an electronic circuit which executes an input/output process of information.
  • the input/output interface 14 receives, as a specific example, information input from a keyboard. Further, the input/output interface 14 transmits the information to a display device.
  • the communication interface 15 is an electronic circuit which executes a process of communicating information with a connection destination via the signal line.
  • the communication interface 15 is, as a specific example, a communication chip for Ethernet (registered trademark) or an NIC (Network Interface Card).
  • unit of the information acquisition unit 100 and the information processing unit 110 may be read as “circuit”, “step”, “procedure”, or “process”.
  • FIG. 3 illustrates a functional configuration diagram of the management support apparatus 10 according to the first embodiment.
  • the management support apparatus 10 includes the information acquisition unit 100 and the information processing unit 110 . Further, the information acquisition unit 100 includes a measurement-information acquisition unit 101 and an input-information acquisition unit 102 . Further, the information processing unit 110 includes a threshold-value calculation unit 111 , a probability calculation unit 112 , a probability-distribution derivation unit 113 , and a replacement-quantity calculation unit 114 .
  • the information acquisition unit 100 acquires information to be used for processing by the information processing unit 110 .
  • the measurement-information acquisition unit 101 acquires via the communication interface 15 , the pieces of measurement information regarding the deterioration degrees of the parts used in the devices 20 , which have been measured by the sensors 30 .
  • the input-information acquisition unit 102 acquires via the input/output interface 14 , pieces of input information regarding calculation of a replacement quantity which is the quantity of parts required to be replaced on a next maintenance date. Further, the input-information acquisition unit 102 may acquire the pieces of input information regarding the calculation of the replacement quantity, which have been stored in the auxiliary storage device 13 .
  • the information processing unit 110 calculates the replacement quantity based on the pieces of measurement information regarding the deterioration degrees of the parts acquired by the information acquisition unit 100 and the pieces of input information regarding the calculation of the replacement quantity acquired by the input-information acquisition unit 102 .
  • the threshold-value calculation unit 111 performs the deterioration prediction, using the deterioration degrees of the parts on the next maintenance date, and derives succeeding-deterioration-degree probability distributions which are probability distributions of the deterioration degrees of the parts on a maintenance date after the next. Further, the threshold-value calculation unit 111 calculates based on the succeeding-deterioration-degree probability distributions, replacement deterioration threshold values which are replacement determination criteria for determining whether or not the parts are required to be replaced on the next maintenance date.
  • the deterioration prediction according to the first embodiment indicates a process of predicting the probability distributions of the deterioration degrees at an arbitrary time point in the future, based on the deterioration degrees of the parts at some time point.
  • the probability calculation unit 112 performs the deterioration prediction, using the measurement information on the deterioration degree of each of the plurality of parts of the devices 20 , which has been acquired by the measurement-information acquisition unit 101 , and derives deterioration-degree probability distributions which are probability distributions of the deterioration degrees of the parts on the next maintenance date. Further, the probability calculation unit 112 calculates deterioration-threshold-value probabilities based on the derived deterioration-degree probability distributions.
  • the deterioration-threshold-value probability is a probability that the deterioration degree of the part on the next maintenance date is equal to or larger than the replacement deterioration threshold value.
  • the probability-distribution derivation unit 113 derives a probability distribution of the replacement quantity, using the deterioration-threshold-value probabilities calculated by the probability calculation unit 112 as measurement results (hereinafter, referred to as the measurement results) obtained by measuring the deterioration degrees resulted from usage of the parts in a usage environment where the parts are used.
  • the replacement-quantity calculation unit 114 calculates a replacement quantity with which a lower probability of the probability distribution of the replacement quantity derived by the probability-distribution derivation unit 113 is equal to or larger than a criterion value.
  • the replacement-quantity calculation unit 114 stores in the auxiliary storage device 13 , the calculated replacement quantity as the necessary minimum inventory quantity of replacing parts. Further, not limited to the above descriptions, the replacement-quantity calculation unit 114 may output the calculated replacement quantity to the display device or the like via the input/output interface 14 . Alternatively, the replacement-quantity calculation unit 114 may transmit the calculated replacement quantity to a connection destination via the communication interface 15 .
  • a required reliability degree which is one of the pieces of input information acquired by the input-information acquisition unit 102 is used.
  • the required reliability degree is an index indicating reliability of the management system 1 which manages the inventory quantity of replacing parts. More specifically, the required reliability degree indicates a probability that an inventory shortage occurs in the management system 1 .
  • a manager of the management system 1 can properly estimate the necessary minimum inventory quantity of replacing parts which assures that the inventory shortage does not occur with a certain probability.
  • the replacement-quantity calculation unit 114 calculates the replacement quantity which satisfies the required reliability degree which is 90%, the inventory shortage is assured not to occur with a 90% probability if a calculated replacement quantity of replacing parts are prepared by the maintenance date.
  • step S 100 the measurement-information acquisition unit 101 acquires the measured deterioration degrees and a measurement date of the deterioration degrees as the pieces of measurement information regarding the deterioration degrees of the parts of the devices 20 .
  • the input-information acquisition unit 102 acquires as the pieces of input information regarding the calculation of the replacement quantity, the quantity of devices using the parts, the next maintenance date, the maintenance date after the next, threshold values (hereinafter, referred to as replacement-limit threshold values) of the deterioration degrees indicating limits regarding the replacement, criterion values (hereinafter, referred to as criterion values for upper probabilities) for upper probabilities of the replacement-limit threshold values, and the required reliability degree.
  • replacement-limit threshold values threshold values of the deterioration degrees indicating limits regarding the replacement
  • criterion values hereinafter, referred to as criterion values for upper probabilities
  • the replacement-limit threshold value is a limit value on the deterioration degree at which the part can be used without replacement. If the deterioration degree of the part exceeds the replacement-limit threshold value, the part may malfunction, and the part needs to be replaced immediately without waiting for the next maintenance date.
  • the criterion value for the upper probability is a probability used as a criterion compared with the upper probability that the deterioration degree is equal to or larger than the limit-deterioration threshold value in the probability distribution of the deterioration degree.
  • step S 110 the threshold-value calculation unit 111 calculates the replacement deterioration threshold values. Further, the probability calculation unit 112 calculates the deterioration-threshold-value probabilities, using the replacement deterioration threshold values calculated by the threshold-value calculation unit 111 . Details of calculation processes of the replacement deterioration threshold values and the deterioration-threshold-value probabilities will be described later.
  • step S 120 the probability-distribution derivation unit 113 derives the probability distribution of the replacement quantity, using the deterioration-threshold-value probabilities calculated by the probability calculation unit 112 . Details of a derivation process of the probability distribution of the replacement quantity will be described later.
  • step S 130 the replacement-quantity calculation unit 114 calculates the replacement quantity which satisfies the required reliability degree, using the probability distribution of the replacement quantity derived by the probability-distribution derivation unit 113 . Then, the replacement-quantity calculation unit 114 outputs the calculated replacement quantity as the necessary minimum inventory quantity of replacing parts. Details of a calculation process of the replacement quantity which satisfies the required reliability degree will be described later.
  • the descriptions will be given, using an example in which measurement of the deterioration degrees is performed on all of the N parts on the same date and further, a process of the management support apparatus 10 is performed on the same date as the measurement date.
  • the measurement date may be different depending on each of N devices 20 , and the measurement date and a date to perform the process of the management support apparatus 10 may be different from each other.
  • step S 200 the threshold-value calculation unit 111 substitutes the pieces of information acquired by the information acquisition unit 100 .
  • the threshold-value calculation unit 111 substitutes the quantity of devices for N, the measurement date of the deterioration degrees for to, the next maintenance date for t, the maintenance date after the next for t′, the replacement-limit threshold value for X th , and the criterion value for the upper probability for PD th .
  • the threshold-value calculation unit 111 assigns to each of the N devices 20 , each of numbers from 1 to N to be used for repeating a process, one by one without duplication.
  • steps S 210 a to 210 b constitutes a loop process in which the probability calculation unit 112 calculates the deterioration-threshold-value probability P E,i (i:1 to N) of the part of each of the N devices 20 .
  • step S 210 a the threshold-value calculation unit 111 substitutes 1 as an initial value for i. However, if the process returns from step S 210 b , the threshold-value calculation unit 111 does not substitute 1 as the initial value for i.
  • step S 220 the threshold-value calculation unit 111 substitutes for xi, the measured deterioration degree of the device 20 assigned the number i.
  • the threshold-value calculation unit 111 initializes a search range which is for searching for the replacement deterioration threshold value. Specifically, the threshold-value calculation unit 111 initializes the search range, by setting a variable x for the deterioration degree to be used for search, substituting x i for a minimum value x min and substituting X th for a maximum value x max , as the search range.
  • step S 240 the threshold-value calculation unit 111 substitutes a value for the variable x. Specifically, the threshold-value calculation unit 111 substitutes (x min +x max )/2 for the variable x.
  • steps S 250 a to 250 b constitutes a loop process in which the threshold-value calculation unit 111 changes the search range and searches for the replacement deterioration threshold value.
  • step S 250 a the threshold-value calculation unit 111 sets the deterioration degree on a next maintenance date t as a variable x, and derives the succeeding-deterioration-degree probability distribution which is the probability distribution of the deterioration degree on a maintenance date after the next t′.
  • the threshold-value calculation unit 111 loads from the auxiliary storage device 13 , a program which executes the deterioration prediction.
  • the program which executes the deterioration prediction is at least a program which derives the probability distribution of the deterioration degree at a time point in the future, using a reference time point and the deterioration degree at the reference time point.
  • the program which executes the deterioration prediction may derive the probability distribution of the deterioration degree at the time point in the future, using information on the surrounding such as weather and temperature, or information such as usage frequencies of the parts.
  • the threshold-value calculation unit 111 derives the succeeding-deterioration-degree probability distribution on the maintenance date after the next t′, using the deterioration degree x and the next maintenance date t.
  • the threshold-value calculation unit 111 calculates P D (x th,t ′
  • x,t) indicates an upper probability that the deterioration degree on the maintenance date after the next t′ is equal to or larger than x th when the deterioration degree on the next maintenance date t is x.
  • the threshold-value calculation unit 111 performs same-value determination to check whether or not a criterion value P Dth for the upper probability and P D (x th,t ′
  • the threshold-value calculation unit 111 updates the variable x. Specifically, the threshold-value calculation unit 111 updates the variable x by substituting (x min +x max )/2 for x. Then, the process returns to step S 250 a . However, if the process is not a return from step S 250 b , the variable x is not updated.
  • the threshold-value calculation unit 111 may regard P Dth and P D (x th,t ′
  • step S 260 the threshold-value calculation unit 111 checks whether or not P Dth is larger than P D (x th,t ′
  • step S 270 If it is confirmed that P Dth is larger than P D (x th ,t′
  • step S 270 the threshold-value calculation unit 111 updates the maximum value of the search range and narrows the search range. Specifically, the threshold-value calculation unit 111 substitutes x for x max .
  • step S 280 the threshold-value calculation unit 111 updates the minimum value of the search range and narrows the search range. Specifically, the threshold-value calculation unit 111 substitutes x for x min .
  • step S 250 b the process returns to step S 250 a.
  • step S 290 the probability calculation unit 112 derives the deterioration-degree probability distribution which is a probability distribution of the deterioration degree on the next maintenance date t, and calculates a deterioration threshold-value probability P E,i .
  • the probability calculation unit 112 loads from the auxiliary storage device 13 , the program which executes the deterioration prediction.
  • the probability calculation unit 112 derives the deterioration-degree probability distribution on the next maintenance date t, using the deterioration degree x i and a measurement date to of the deterioration degree.
  • the probability calculation unit 112 calculates P D (x,t
  • x i ,t 0 ) indicates an upper probability that the deterioration degree on the next maintenance date t is equal to or larger than the replacement deterioration threshold value x when the deterioration degree on the measurement date to of the deterioration degree is x i .
  • the probability calculation unit 112 substitutes P D (x,t
  • step S 210 b the probability calculation unit 112 checks whether or not i is N.
  • step S 300 the probability-distribution derivation unit 113 initializes a probability P Q [k](k:0 to N) of the replacement quantity. More specifically, the probability-distribution derivation unit 113 substitutes 1.0 for P Q [0] and 0.0 for each of P Q [1] to P Q [N].
  • step S 310 a the probability-distribution derivation unit 113 substitutes 1 for i, as an initial value. However, if the process returns from step S 310 b , the probability-distribution derivation unit 113 does not substitute 1 for i, as the initial value.
  • step S 320 a the probability-distribution derivation unit 113 substitutes N for the replacement quantity k, as an initial value. However, if the process returns from step S 320 b , the probability-distribution derivation unit 113 does not substitute N for the replacement quantity k, as the initial value.
  • step S 330 the probability-distribution derivation unit 113 updates the probability P Q [k] of the replacement quantity that the replacement quantity is k.
  • P Q [k] is a sum of a probability in a case where the replacement quantity for devices up to an i ⁇ 1-th device 20 is k and replacement is not performed on an i-th device 20 and a probability in a case where the replacement quantity for the devices up to the i ⁇ 1-th device 20 is k ⁇ 1 and replacement is performed on the i-th device 20 . Therefore, the probability-distribution derivation unit 113 updates P Q [k] by substituting P Q [k]*(1 ⁇ P E,i )+PQ[k ⁇ 1]*P E,i for P Q [k].
  • step S 320 b the probability-distribution derivation unit 113 checks whether or not the replacement quantity k is 1.
  • step S 340 If it is confirmed that k is 1, the process proceeds to step S 340 .
  • step S 320 a the probability-distribution derivation unit 113 subtracts 1 from k. Then, the process returns to step S 320 a.
  • step S 340 the probability-distribution derivation unit 113 updates a probability P Q [0] of the replacement quantity that the replacement quantity is 0.
  • P Q [0] is a probability in a case where the replacement quantity for the devices up to the i ⁇ 1-th device 20 is 0 and the replacement is not performed also on the i-th device 20 . Therefore, the probability-distribution derivation unit 113 updates P Q [0] by substituting P Q [0]*(1 ⁇ P E,i ) for P Q [0].
  • step S 310 b the probability-distribution derivation unit 113 checks whether or not i is N.
  • the probability-distribution derivation unit 113 adds 1 to i. Then, the process returns to step S 310 a.
  • step S 400 the replacement-quantity calculation unit 114 substitutes the acquired required reliability degree for P Qreq .
  • step S 410 the replacement-quantity calculation unit 114 initializes a variable P by substituting 0.0 for the variable P used as a temporary cumulative value of the lower probability.
  • step S 420 a the replacement-quantity calculation unit 114 substitutes 0 for the replacement quantity k, as an initial value. However, if the process returns from step S 20 b , the replacement-quantity calculation unit 114 does not substitute 0 for the replacement quantity k, as the initial value.
  • step S 430 the replacement-quantity calculation unit 114 updates the variable P.
  • the replacement-quantity calculation unit 114 updates the variable P by substituting P+P Q [k] for P.
  • step S 440 the replacement-quantity calculation unit 114 checks whether or not P is equal to or larger than a required reliability degree P Qreq .
  • step S 450 If it is confirmed that P is equal to or larger than the required reliability degree P Qreq , the process proceeds to step S 450 .
  • step S 420 b the process proceeds to step S 420 b.
  • step S 420 b the replacement-quantity calculation unit 114 adds 1 to k. Then, the process returns to step S 420 a.
  • step S 450 the replacement-quantity calculation unit 114 outputs k as the replacement quantity which satisfies the required reliability degree P Qreq .
  • the management support apparatus 10 calculates the replacement quantity which satisfies the required reliability degree, based on the measurement results. Therefore, the necessary minimum inventory quantity of replacing parts is properly estimated.
  • the replacing parts can be obtained readily if the replacing parts are commodities, however, in many cases, custom-made parts are used in systems of social infrastructures such as railway cars, a power plant, and an elevator. Then, lead time taken for preparing the inventory of the custom-made parts tends to be longer than that for the commodities, and it is very important to precisely estimate the necessary inventory quantity as early as possible.
  • the necessary minimum inventory quantity of replacing parts are properly estimated by the management support apparatus 10 , therefore, the manager of the inventory can reduce the financial burden resulted from the excess inventory, while preventing the damage resulted from the inventory shortage of the replacing parts.
  • the next maintenance date and the maintenance date after the next are used respectively.
  • the replacement timing and the succeeding replacement timing may be any maintenance date and any maintenance date after the maintenance date respectively.
  • the replacement timing not only the maintenance date, but also any form of unit of time such as maintenance time, a maintenance time range, a maintenance week, and a maintenance month may be applied to the replacement timing.
  • the management support apparatus 10 outputs the replacement quantity as the process result of the necessary minimum inventory quantity of replacing parts, but not limited to this, the management support apparatus 10 may output the replacement deterioration threshold values, the deterioration-threshold-value probabilities, and the probability distribution of the replacement quantity.
  • a second embodiment will be described with reference to FIGS. 8 and 9 .
  • an example will be described of calculating the necessary minimum inventory quantity of replacing parts.
  • the example is premised that delivery dates when shipped replacing parts are delivered to the usage environment come irregularly, consequently, the next maintenance date and the maintenance date after the next when the parts are replaced come irregularly.
  • a specific example is an example in which a fixed date is not set for adding the replacing parts due to a matter of transportation such as a truck, a railway, and an airplane, consequently, the maintenance date comes irregularly.
  • FIG. 8 illustrates a functional configuration example of the management support apparatus 10 according to the second embodiment.
  • the input-information acquisition unit 102 newly acquires a delivery plan describing delivery dates via the input/output interface 14 or the communication interface 15 .
  • the management support apparatus 10 newly includes a replacement-timing inference unit 115 .
  • the replacement-timing inference unit 115 checks the delivery dates described in the delivery plan and infers a candidate date for the next maintenance date and a candidate date for the maintenance date after the next.
  • FIG. 9 is a flowchart illustrating an operation example of the management support apparatus 10 according to the second embodiment.
  • step S 500 the measurement-information acquisition unit 101 acquires the measured deterioration degrees and the measurement date of the deterioration degrees as the pieces of measurement information regarding the deterioration degrees of the parts of the devices 20 . Further, the input-information acquisition unit 102 acquires the replacement-limit threshold values, the criterion values for the upper probabilities, and the required reliability degree as the pieces of input information regarding the calculation of the replacement quantity.
  • the input-information acquisition unit 102 acquires the delivery plan describing the delivery dates.
  • step S 510 the replacement-timing inference unit 115 infers the next maintenance date and the maintenance date after the next based on the delivery dates.
  • the replacement-timing inference unit 115 checks a next delivery date and a delivery date after the next which are described in the delivery plan. Then, the replacement-timing inference unit 115 infers a day after the next delivery date as the next maintenance date. Further, the replacement-timing inference unit 115 infers a day after the delivery date after the next as the maintenance date after the next.
  • the maintenance date is not limited to the day after the delivery date, the number of days between the delivery date and the maintenance date may be input via the input/output interface 14 , and the replacement-timing inference unit 115 may infer the next maintenance date and the maintenance date after the next, using the input number of days.
  • the replacement-timing inference unit 115 may infer a plurality of next maintenance dates and a plurality of maintenance dates after the next.
  • steps S 110 to S 130 are the same as those described in the first embodiment, descriptions thereof will be omitted.
  • steps S 110 to S 130 may be performed for each combination.
  • the management support apparatus 10 properly estimates the necessary minimum inventory quantity of replacing parts also when the delivery dates come irregularly, consequently, the next maintenance date and the maintenance date after the next come irregularly. As a result, it is possible to prevent the inventory shortage and the excess inventory.
  • the labor required for the estimation based on the personal experience of the manager of the inventory is equal to or more than that in the first embodiment. Therefore, in the second embodiment, an effect of more reduction in the labor is expected.
  • a third embodiment will be described with reference to FIG. 10 .
  • a specific example is an example in which a different inspection date is set for each device in which a part is placed, depending on each device, like parts placed in a composition of train cars, accordingly, the maintenance date of each is different.
  • FIG. 10 is a flowchart illustrating an operation example of the management support apparatus 10 according to the third embodiment.
  • the number of groups is not limited to two, and the number of groups may be equal to or more than two.
  • the quantity of devices 20 classified into each group is not limited to N, and as a result of classification based on the next maintenance date and the maintenance date after the next, each group may have a different quantity of devices 20 .
  • step S 600 the measurement-information acquisition unit 101 acquires the measured deterioration degrees and the measurement date of the deterioration degrees as the pieces of measurement information regarding the deterioration degrees of the parts of the devices 20 .
  • the input-information acquisition unit 102 acquires the replacement-limit threshold values, the criterion values for the upper probabilities, and the required reliability degree as the pieces of input information regarding the calculation of the replacement quantity.
  • the input-information acquisition unit 102 acquires for each device, the next maintenance date and the maintenance date after the next. Then, the input-information acquisition unit 102 classifies the devices 20 into two groups each of which is to have the N devices 20 which have the same next maintenance date and the same maintenance date after the next.
  • steps S 610 a to S 610 b constitutes a loop process of calculating the replacement quantity for each of the two groups.
  • step S 610 a the threshold-value calculation unit 111 selects one of the two groups. Then, the threshold-value calculation unit 111 performs the calculation processes of the replacement deterioration threshold values of the parts of the devices 20 in the selected group.
  • steps S 110 to S 130 are the same as those described in the first embodiment and the second embodiment, descriptions thereof will be omitted.
  • step S 610 b the replacement-quantity calculation unit 114 checks whether or not both of the two groups have been selected.
  • step S 610 a If it is not confirmed that both of the two groups have been selected, the process returns to step S 610 a.
  • the management support apparatus 10 properly estimates the necessary minimum inventory quantity of replacing parts also when the next maintenance date and the maintenance date after the next are different depending on each device. As a result, it is possible to prevent the inventory shortage and the excess inventory.
  • a fourth embodiment will be described.
  • an example will be described of calculating the necessary minimum inventory quantity of replacing parts.
  • the example is premised that each of a location of the usage environment and an inventory storage place of the replacing part for the part used by the device is different depending on each part and the maintenance date is different depending on each usage environment.
  • a specific example is an example in which there are a plurality of usage environments which are geographically far from each other and a location of the inventory storage place and each of the usage environments are geographically far from each other, like elevators.
  • FIG. 11 illustrates a functional configuration example of the management support apparatus 10 according to the fourth embodiment.
  • the management support apparatus 10 newly includes a delivery-date inference unit 116 .
  • the delivery-date inference unit 116 infers a delivery date based on at least one of pieces of information regarding inference of the delivery date, such as the location of the usage environment, a distance from the usage environment to delivery origin of the replacing part, and the number of days required for delivering the replacing part.
  • the input-information acquisition unit 102 newly acquires the pieces of information regarding the inference of the delivery date via the input/output interface 14 or the communication interface 15 .
  • FIG. 12 is a flowchart illustrating an operation example of the management support apparatus 10 according to the fourth embodiment.
  • the number of usage environments is not limited to two and may be equal to or more than two. Further, the quantity of devices 20 used in each usage environment is not limited to N, and each of usage environments may have a different quantity of devices 20 from each other.
  • step S 700 the measurement-information acquisition unit 101 acquires the measured deterioration degrees and the measurement date of the deterioration degrees as the pieces of measurement information regarding the deterioration degrees of the parts of the devices 20 .
  • the input-information acquisition unit 102 acquires the replacement-limit threshold values, the criterion values for the upper probabilities, and the required reliability degree as the pieces of input information regarding the calculation of the replacement quantity.
  • the input-information acquisition unit 102 acquires the pieces of information regarding the inference of the delivery dates.
  • step S 710 the delivery-date inference unit 116 infers the delivery date when the replacing part is delivered to each usage environment, based on the acquired pieces of information regarding the inference of the delivery date.
  • the delivery-date inference unit 116 infers the delivery date based on the number of days required for delivering the replacing part.
  • the delivery-date inference unit 116 infers the delivery date, by reading from the auxiliary storage device 13 , a comparison table between the locations of the usage environments of the parts and the respective numbers of days required for delivering the replacing part.
  • the delivery-date inference unit 116 infers the delivery date, by reading from the auxiliary storage device 13 , a comparison table between the distances from the locations of the usage environments of the parts to the respective delivery origins, and the respective numbers of days required for delivering the replacing part.
  • step S 720 the replacement-timing inference unit 115 infers the next maintenance date and the maintenance date after the next for each usage environment based on the delivery date inferred for each usage environment.
  • the replacement-timing inference unit 115 infers for each usage environment, a day after the next delivery date as the next maintenance date and a day after the delivery date after the next as the maintenance date after the next.
  • the maintenance date is not limited to the day after the delivery date, the number of days from the delivery date to the maintenance date may be input via the input/output interface 14 , and the replacement-timing inference unit 115 may infer the next maintenance date and the maintenance date after the next.
  • the replacement-timing inference unit 115 may infer for each usage environment, a plurality of next maintenance dates and a plurality of maintenance dates after the next.
  • steps S 730 a to S 730 b constitutes a loop process of calculating for each of the two usage environments, the replacement quantity on each corresponding maintenance date.
  • step S 730 a the threshold-value calculation unit 111 selects one of the two usage environments. Then, the threshold-value calculation unit 111 performs the calculation processes of the replacement deterioration threshold values of the parts of the devices 20 in the selected usage environment.
  • steps S 110 to S 130 are the same as those described in the first embodiment and the second embodiment, descriptions thereof will be omitted.
  • steps S 110 to S 130 may be performed for each combination.
  • step S 730 b the replacement-quantity calculation unit 114 checks whether or not both of the two usage environments have been selected.
  • step S 730 a If it is not confirmed that both of the two groups have been selected, the process returns to step S 730 a.
  • the management support apparatus 10 properly estimates the necessary minimum inventory quantity of replacing parts also when the location of the usage environment and the inventory storage place of the replacing part are different depending on each part and the maintenance date is different depending on each usage environment. As a result, it is possible to prevent the inventory shortage and the excess inventory.
  • a fifth embodiment will be described with reference to FIGS. 13 to 16 .
  • the usage environment where the parts are used is constituted by a plurality of usage places and the probability distribution of the replacement quantity for the usage place is derived for each of the plurality of usage places.
  • an example will be described of calculating the necessary minimum inventory quantity of replacing parts, using the derived probability distribution of the replacement quantity for each of the plurality of usage places.
  • the example is premised that the inventory quantities of replacing parts in the usage environment are centrally managed.
  • a specific example is an example in which each of a plurality of factories manages its inventory quantity of replacing parts and one central management warehouse manages the replacing parts to be delivered to each of the factories, like a production device in a factory.
  • FIG. 13 is a configuration diagram of a central management system 2 according to the fifth embodiment.
  • the central management system 2 includes the management support apparatus 10 , the network 40 , and sites 50 .
  • the site 50 is the usage place where the parts of the plurality of devices 20 are used.
  • the site 50 is a place such as a factory or a power plant.
  • the management systems 1 in the first embodiment are placed. Further, the management support apparatuses 10 in the management systems 1 in the sites 50 and the management support apparatus 10 in the central management system 2 are connected to each other via the network 40 .
  • the number of sites 50 is two, but not limited to two, the number of sites 50 may be equal to or more than two.
  • management support apparatuses 10 and the network 40 are the same as those described in the first embodiment, descriptions thereof will be omitted.
  • FIG. 14 illustrates a functional configuration example of the management support apparatus 10 according to the fifth embodiment.
  • the measurement-information acquisition unit 101 acquires as the measurement results, the probability distributions of the replacement quantities for the sites 50 from the management support apparatuses 10 in the sites 50 via the communication interface 15 .
  • the probability-distribution derivation unit 113 derives the probability distribution of the replacement quantity for the usage environment, using the probability distributions of the replacement quantities for the sites 50 which have been acquired by the measurement-information acquisition unit 101 .
  • FIG. 15 is a flowchart illustrating an operation example of the management support apparatus 10 according to the fifth embodiment.
  • step S 800 the measurement-information acquisition unit 101 acquires the probability distributions of the replacement quantities for the sites 50 , as the pieces of measurement information.
  • the input-information acquisition unit 102 acquires the replacement-limit threshold values, the criterion values for the upper probabilities, and the required reliability degrees, as the pieces of input information regarding the calculation of the replacement quantities.
  • step S 810 the probability-distribution derivation unit 113 derives the probability distribution of the replacement quantity for the usage environment, using the probability distributions of the replacement quantities for the sites 50 which have been acquired by the measurement-information acquisition unit 101 . Details of the derivation process of the probability distribution of the replacement quantity according to the fifth embodiment will be described later.
  • step S 130 is the same as that described in the first embodiment, descriptions thereof will be omitted.
  • FIG. 16 is a flowchart illustrating an example of the derivation process of the probability distribution of the replacement quantity for the usage environment by the probability-distribution derivation unit 113 of the management support apparatus 10 according to the fifth embodiment.
  • step S 900 the probability-distribution derivation unit 113 substitutes the probability of the replacement quantity, from the probability distribution of the replacement quantity for the site 50 .
  • the probability-distribution derivation unit 113 assigns to each of the M sites 50 , each of numbers from 1 to M to be used for repeating a process, one by one without duplication.
  • the probability-distribution derivation unit 113 checks a range of the replacement quantity from the probability distribution of the replacement quantity for the site 50 being assigned a number j (j:1 to M), and substitutes a maximum value for
  • the probability-distribution derivation unit 113 substitutes for P Q,j [k J ], a probability that the replacement quantity is k a (k a : 0 to N a ).
  • step S 910 the probability-distribution derivation unit 113 initializes the probability distribution of the replacement quantity for the usage environment.
  • the probability-distribution derivation unit 113 calculates a maximum value N c of the replacement quantity for the usage environment, using a formula 1.
  • the probability-distribution derivation unit 113 substitutes 1.0 for a probability P Q,c [0] that the replacement quantity is 0, as the probability of the replacement quantity for the usage environment, to initialize the probability P Q ,c[0]. Further, the probability-distribution derivation unit 113 substitutes 0.0 for a probability P Q,c [k c ] that the replacement quantity is kc (kc: 1 to Ne), to initialize the probability P Q,c [k c ],
  • step S 920 a the probability-distribution derivation unit 113 substitutes 1 for j, as an initial value. However, if the process returns from step S 920 b , the probability-distribution derivation unit 113 does not substitutes 1 for j, as the initial value.
  • step S 930 a the probability-distribution derivation unit 113 substitutes N c for k c , as an initial value. However, if the process returns from step S 930 b , the probability-distribution derivation unit 113 does not substitutes N c for k c , as the initial value.
  • step S 940 the probability-distribution derivation unit 113 substitutes 0.0 for a variable tmp which is used as a temporary cumulative value of the probability of the replacement quantity, to initialize the variable tmp.
  • step S 950 a the probability-distribution derivation unit 113 substitutes 0 for k j , as an initial value. However, if the process returns from step S 950 b , the probability-distribution derivation unit 113 does not substitute 0 for k j , as the initial value.
  • step S 960 the probability-distribution derivation unit 113 checks whether or not the replacement quantity L for the usage environment is equal to or larger than the replacement quantity is, for the site 50 being assigned the number j.
  • step S 970 If it is confirmed that L is equal to or larger than k j , the process proceeds to step S 970 .
  • step S 980 the process proceeds to step S 980 .
  • step S 970 the probability-distribution derivation unit 113 updates the variable tmp.
  • the probability-distribution derivation unit 113 updates the variable tmp, by substituting tmp+P Q,c [k c ⁇ k j ]*P Q,j [k j ] for tmp.
  • step S 950 b the probability-distribution derivation unit 113 adds 1 to k j . Then, the process returns to S 950 a.
  • step S 980 the probability-distribution derivation unit 113 substitutes the variable tmp for the probability P Q,c [k c ] of the replacement quantity L.
  • step S 930 b the probability-distribution derivation unit 113 checks whether or not L is 0.
  • step S 920 b If it is confirmed that L is 0, the process proceeds to step S 920 b.
  • the probability-distribution derivation unit 113 subtracts 1 from L. Then, the process returns to step S 930 a.
  • step S 920 b the probability-distribution derivation unit 113 checks whether or not j is M.
  • the probability-distribution derivation unit 113 notifies the replacement-quantity calculation unit 114 of the derived probability distribution of the replacement quantity for the usage environment.
  • the usage environment where the parts are used is constituted by the plurality of sites 50 , and the probability distribution of the replacement quantity for the site 50 is derived for each of the plurality of sites 50 . Then, the management support apparatus 10 properly estimates the necessary minimum inventory quantity of replacing parts, using the derived probability distributions of the replacement quantities for the sites 50 , also when the inventory quantities of replacing parts in the usage environment are centrally managed. As a result, it is possible to prevent the inventory shortage and the excess inventory.
  • the fifth embodiment can be applied to not only a case where a single company manages the inventory quantities in a central warehouse and subordinate warehouses, but also a case of business in a medicine selling method in Toyama. More specifically, the manufacturer can contract a client for a service-level agreement including the required reliability degree, and the manufacturer can manage its own warehouse of factories and a warehouse of the client based on the required reliability degree.
  • one of these embodiments may be partially implemented.
  • management support apparatus 10 of FIG. 2 although the functions of the management support apparatus 10 are realized by software, the functions of the management support apparatus 10 may be realized by hardware.
  • FIG. 17 illustrates a configuration in which the functions of the management support apparatus 10 are realized by the hardware.
  • An electronic circuit 90 in FIG. 17 is a dedicated electronic circuit for realizing the functions of the information acquisition unit 100 and the information processing unit 110 in the management support apparatus 10 .
  • the electronic circuit 90 is connected to a signal line 91 .
  • the electronic circuit 90 is a single circuit, a composite circuit, a programmed processor, a parallel-programmed processor, a logic IC, a GA, an ASIC, or an FPGA.
  • GA stands for Gate Array.
  • ASIC stands for Application Specific Integrated Circuit.
  • FPGA stands for Field-Programmable Gate Array.
  • Functions of components of the management support apparatus 10 may be realized by one electronic circuit or realized by being distributed among a plurality of electronic circuits. Further, a portion of the functions of the components of the management support apparatus 10 may be realized by the electronic circuit, and the rest of the functions may be realized by the software.
  • Each of the processor 11 and the electronic circuit 90 is also referred to as processing circuitry.
  • the functions of the information acquisition unit 100 and the information processing unit 110 may be realized by the processing circuitry.
  • 1 management system
  • 2 central management system
  • 10 management support apparatus
  • 11 processor
  • 12 memory
  • 13 auxiliary storage device
  • 14 input/output interface
  • 15 communication interface
  • 20 device
  • 30 sensor
  • 40 network
  • 50 site
  • 100 information acquisition unit
  • 101 measurement-information acquisition unit
  • 102 input-information acquisition unit
  • 110 information processing unit
  • 111 threshold-value calculation unit
  • 112 probability calculation unit
  • 113 probability-distribution derivation unit
  • 114 replacement-quantity calculation unit
  • 115 replacement-timing inference unit
  • 116 delivery-date inference unit.

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060053123A1 (en) * 2004-08-02 2006-03-09 International Business Machines Corporation Anomaly detection based on directional data
US20080215628A1 (en) * 2007-01-29 2008-09-04 Seiji Adachi Replacement part order processing apparatus, method for ordering replacement parts and computer-readable recording medium
US7672921B1 (en) * 2004-02-25 2010-03-02 Rockwell Automation Technologies, Inc. Systems and methods for automatically determining and/or inferring component end of life (EOL)
US20180247256A1 (en) * 2017-02-28 2018-08-30 Fanuc Corporation Inventory management system having functions of performing inventory management and preventive maintenance

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4282717B2 (ja) * 2006-12-27 2009-06-24 株式会社東芝 定期点検データ分析装置およびその方法
JP5256089B2 (ja) * 2009-03-26 2013-08-07 大阪瓦斯株式会社 部品需要予測方法、部品需要予測システム
JP2017167847A (ja) * 2016-03-16 2017-09-21 株式会社東芝 運用計画案作成装置、運用計画案作成方法、プログラムおよび運用計画案作成システム

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7672921B1 (en) * 2004-02-25 2010-03-02 Rockwell Automation Technologies, Inc. Systems and methods for automatically determining and/or inferring component end of life (EOL)
US20060053123A1 (en) * 2004-08-02 2006-03-09 International Business Machines Corporation Anomaly detection based on directional data
US20080215628A1 (en) * 2007-01-29 2008-09-04 Seiji Adachi Replacement part order processing apparatus, method for ordering replacement parts and computer-readable recording medium
US20180247256A1 (en) * 2017-02-28 2018-08-30 Fanuc Corporation Inventory management system having functions of performing inventory management and preventive maintenance

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